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Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials

, , , , , and . Physical Review Materials, (November 2024)
DOI: 10.1103/physrevmaterials.8.115407

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Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials, , , , , and . Physical Review Materials, (November 2024)Data for: Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials, , , , , and . Dataset, (2024)Related to: Yongliang Ou, Yuji Ikeda, Lena Scholz, Sergiy Divinski, Felix Fritzen, Blazej Grabowski (2024). Atomistic modeling of bulk and grain boundary diffusion in solid electrolyte Li6PS5Cl using machine-learning interatomic potentials. arXiv: 2407.04126.A Compositional Semantics for Modelica-style Variable-structureModeling., , , and . EOOLT, volume 56 of Linköping Electronic Conference Proceedings, page 45-54. Linköping University Electronic Press, (2011)Code for effective heat conductivity in thin porous media, and . Dataset, (2021)Related to: Scholz, L., Bringedal, C. A Three-Dimensional Homogenization Approach for Effective Heat Transport in Thin Porous Media. Transp Porous Med (2022). doi: 10.1007/s11242-022-01746-y.The Signature Method for DAEs arising in the modeling of electrical circuits.. J. Computational Applied Mathematics, (2018)A Three-Dimensional Homogenization Approach for Effective Heat Transport in Thin Porous Media, and . Transport in porous media, 141 (3): 737-769 (2022)Self-adjoint differential-algebraic equations., , and . MCSS, 26 (1): 47-76 (2014)Diskriminierung: Wie sexistisch ist die Uni?. Die Zeit, (Aug 3, 2017)